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Abstract Drowsiness associated with sleep loss and circadian misalignment is a risk factor for accidents and human error. The percentage of time that the eyes are more than 80% closed (PERCLOS) is one of the most validated indices used for the passive detection of drowsiness, which is increased with sleep deprivation, after partial sleep restriction, at nighttime, and by other drowsiness manipulations during vigilance tests, simulated driving, and on-road driving. However, some cases have been reported wherein PERCLOS was not affected by drowsiness manipulations, such as in moderate drowsiness conditions, in older adults, and during aviation-related tasks. Additionally, although PERCLOS is one of the most sensitive indices for detecting drowsiness-related performance impairments during the psychomotor vigilance test or behavioral maintenance of wakefulness test, no single index is currently available as an optimal marker for detecting drowsiness during driving or other real-world situations. Based on the current published evidence, this narrative review suggests that future studies should focus on: (1) standardization to minimize differences in the definition of PERCLOS between studies; (2) extensive validation using a single device that utilizes PERCLOS-based technology; (3) development and validation of technologies that integrate PERCLOS with other behavioral and/or physiological indices, because PERCLOS alone may not be sufficiently sensitive for detecting drowsiness caused by factors other than falling asleep, such as inattention or distraction; and (4) further validation studies and field trials targeting sleep disorders and trials in real-world environments. Through such studies, PERCLOS-based technology may contribute to preventing drowsiness-related accidents and human error.

In this study we explore how different levels of emotional intensity (Arousal) and pleasantness (Valence) are reflected in electroencephalographic (EEG) signals. We performed the experiments on EEG data of 32 subjects from the DEAP public dataset, where the subjects were stimulated using 60-s videos to elicitate different levels of Arousal/Valence and then self-reported the rating from 1 to 9 using the self-assessment Manikin (SAM). The EEG data was pre-processed and used as input to a convolutional neural network (CNN). First, the 32 EEG channels were used to compute the maximum accuracy level obtainable for each subject as well as for creating a single model using data from all the subjects. The experiment was repeated using one channel at a time, to see if specific channels contain more information to discriminate between low vs high arousal/valence. The results indicate than using one channel the accuracy is lower compared to using all the 32 channels. An optimization process for EEG channel selection is then designed with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with the objective to obtain optimal channel combinations with high accuracy recognition. The genetic algorithm evaluates all possible combinations using a chromosome representation for all the 32 channels, and the EEG data from each chromosome in the different populations are tested iteratively solving two unconstrained objectives; to maximize classification accuracy and to reduce the number of required EEG channels for the classification process. Best combinations obtained from a Pareto-front suggests that as few as 8–10 channels can fulfill this condition and provide the basis for a lighter design of EEG systems for emotion recognition. In the best case, the results show accuracies of up to 1.00 for low vs high arousal using eight EEG channels, and 1.00 for low vs high valence using only two EEG channels. These results are encouraging for research and healthcare applications that will require automatic emotion recognition with wearable EEG.

Dreams experienced during rapid eye movement (REM) sleep have emotional features. Intervention methods for dream affectivity have recently garnered interest; we previously demonstrated that negative dreams were induced during REM sleep by exposure to favorable or familiar odors. However, the underlying mechanisms behind this phenomenon remain unclear. Thus, to address this gap, we investigated whether more intense odors could induce negative dreams, as odors tend to be perceived as more intense when they are preferred or familiar. Contrary to our hypothesis, the results of our study indicated that subjective intense odors did not induce negative dreams. We initially anticipated stronger odors to have a greater impact on dream emotionality, as they stimulate the brain more intensely. Notably, during arousal, weak odors tended to evoke a more potent olfactory response, while strong odors tended to produce a weaker response. To investigate whether this difference influenced the effects on dreams, we compared the respiratory activities of the strongly and weakly perceived odor groups; however, no significant differences were observed. Our findings suggest that subjectively perceived strong odors are unlikely to affect dream emotionality and may be processed differently than favorable or familiar odors.

In remote areas, visiting a laboratory for sleep testing is inconvenient. We, therefore, developed a Mobile Sleep Lab in a bus powered by fuel cells with two sleep measurement chambers. As the environment in the bus could affect sleep, we examined whether sleep testing in the Mobile Sleep Lab was as feasible as in a conventional sleep laboratory (Human Sleep Lab). We tested 15 healthy adults for four nights using polysomnography (the first two nights at the Human Sleep Lab or Mobile Sleep Lab with a switch to the other facility for the next two nights). Sleep variables of the four measurements were used to assess the discrepancy of different places or different nights. No significant differences were found between the laboratories other than the percentage of total sleep time in stage N3. Next, we analyzed the intraclass correlation coefficient to evaluate the test-retest reliability. The intraclass correlation coefficient between these two measurements: the Human Sleep Lab and Mobile Sleep Lab showed similar reliability for the same sleep variables. The intraclass correlation coefficient revealed that several sleep indexes, such as total sleep time, sleep efficiency, wake after sleep onset, percentage of stage N1, and stage R latency, showed poor reliabilities (<0.5) based on Koo and Li’s criteria. In contrast, the percentage of stage N3 showed moderate (0.5–0.75) or good (0.75–0.9) reliabilities. As almost all sleep variables showed no difference and same level of test-retest reliability between the Mobile Sleep Lab and Human Sleep Lab, the Mobile Sleep Lab might be suitable for conducting polysomnography as a conventional sleep laboratory. The reduction in N3 in the Mobile Sleep Lab should be scrutinized in the larger sample, including sleep disorders. Practical application of the Mobile Sleep Lab can transform sleep medicine in remote areas.

Scoring sleep stages from biological signals is an essential but labor-intensive inspection for sleep diagnosis. The existing automated scoring methods have achieved high accuracy but are not widely applied in clinical practice. In our understanding, the existing methods have failed to establish the trust of sleep experts (e.g., physicians and clinical technologists) due to a lack of ability to explain the evidences/clues for scoring. In this study, we developed a deep-learning-based scoring model with a reasoning mechanism called class activation mapping (CAM) to solve this problem. This mechanism explicitly shows which portions of the signals support our model’s sleep stage decision, and we verified that these portions overlap with the “characteristic waves,” which are evidences/clues used in the manual scoring process. In exchange for the acquisition of explainability, employing CAM makes it difficult to follow some scoring rules. Although we concerned the negative effect of CAM on the scoring accuracy, we have found that the impact is limited. The evaluation experiment shows that the proposed model achieved a scoring accuracy of 86.9 % . It is superior to those of some existing methods and the inter-rater reliability among the sleep experts. These results suggest that Sleep-CAM achieved both explainability and required scoring accuracy for practical usage.

We present a new approach to classifying the sleep stage that incorporates a computationally inexpensive method based on permutations for channel selection and takes advantage of deep learning power, specifically the gated recurrent unit (GRU) model, along with other deep learning methods. By systematically permuting the electroencephalographic (EEG) channels, different combinations of EEG channels are evaluated to identify the most informative subset for the classification of the 5-class sleep stage. For analysis, we used an EEG dataset that was collected at the International Institute for Integrative Sleep Medicine (WPI-IIIS) at the University of Tsukuba in Japan. The results of these explorations provide many new insights such as the (1) drastic decrease in performance when channels are fewer than 3, (2) 3-random channels selected by permutation provide the same or better prediction than the 3 channels recommended by the American Academy of Sleep Medicine (AASM), (3) N1 class suffers the most in prediction accuracy as the channels drop from 128 to 3 random or 3 AASM, and (4) no single channel provides acceptable levels of accuracy in the prediction of 5 classes. The results obtained show the GRU’s ability to retain essential temporal information from EEG data, which allows capturing the underlying patterns associated with each sleep stage effectively. Using permutation-based channel selection, we enhance or at least maintain as high model efficiency as when using high-density EEG, incorporating only the most informative EEG channels.

PurposeThe diffusion tensor image analysis along the perivascular space (DTI-ALPS) method was developed to evaluate the brain’s glymphatic function or interstitial fluid dynamics. This study aimed to evaluate the reproducibility of the DTI-ALPS method and the effect of modifications in the imaging method and data evaluation.Materials and methodsSeven healthy volunteers were enrolled in this study. Image acquisition was performed for this test–retest study using a fixed imaging sequence and modified imaging methods which included the placement of region of interest (ROI), imaging plane, head position, averaging, number of motion-proving gradients, echo time (TE), and a different scanner. The ALPS-index values were evaluated for the change of conditions listed above.ResultsThis test–retest study by a fixed imaging sequence showed very high reproducibility (intraclass coefficient = 0.828) for the ALPS-index value. The bilateral ROI placement showed higher reproducibility. The number of averaging and the difference of the scanner did not influence the ALPS-index values. However, modification of the imaging plane and head position impaired reproducibility, and the number of motion-proving gradients affected the ALPS-index value. The ALPS-index values from 12-axis DTI and 3-axis diffusion-weighted image (DWI) showed good correlation (r = 0.86). Also, a shorter TE resulted in a larger value of the ALPS-index.ConclusionALPS index was robust under the fixed imaging method even when different scanners were used. ALPS index was influenced by the imaging plane, the number of motion-proving gradient axes, and TE in the imaging sequence. These factors should be uniformed in the planning ALPS method studies. The possibility to develop a 3-axis DWI-ALPS method using three axes of the motion-proving gradient was also suggested.

[...]there is no doubt that research is needed to achieve these desired outcomes. Besides using chewing gum to train orofacial muscles, orofacial muscle (lip and tongue) strength measurement devices, such as the Iowa Oral Performance Instrument (IOPI), can also be used for training. [...]I am very grateful to the authors of this Special Issue.

Daytime napping improves performance, which is maximized with a post-N2 9-min nap. We evaluated whether a system that enables optimal-time automatic awakening using blood flow parameters could improve performance, sleepiness, and fatigue compared to no-nap. Additionally, we investigated whether its performance was comparable to manual awakening based on polysomnography. Eighty-one healthy adults (33.6 ± 12.8 years) were randomly assigned to automatic- or manual-awakening or rest groups. A task bout comprising a digit-symbol substitution test (DSST), visual detection test, and sleepiness and fatigue questionnaires was performed three times per session before napping and for six sessions after napping. In all post-nap sessions, sleepiness and fatigue in the automatic awakening group decreased, compared to the rest group, and were comparable to those in the manual awakening group. The DSST improved in the sixth post-nap session for the manual awakening group compared to the rest group; no improvement was observed in the automatic awakening group. The system model was refined by adding training data and tested on 50 healthy adults (40.6 ± 13.1 years). The test results revealed that the N2 detection accuracy of the system improved. The optimal automatic awakening system improves subjective sleepiness and fatigue, and further improvements in its accuracy may enhance post-nap performance.

The goal of the present study was to examine the effect of listening to self-relevant words (i.e., one's own name) on vigilant attention, arousal, and subjective sleepiness during performance of a psychomotor vigilance test (PVT). Twenty-one participants aged 20-26 years (22.2 ± 1.76) performed a PVT in four experimental conditions: one in which their own full name was pronounced every 20 s in the stimuli epochs, one in which their full name was pronounced in inverted form, one in which beeps were played, and a control condition with no stimuli. Listening to personal names reduced attentional lapses during the PVT (i.e., the number of reaction times no less than 500 ms). The results are a first step in applying the name effect to technologies and devices aimed at maintaining arousal levels and preventing accidents during a monotonous task, such as driving.